Doutorado em Química
URI Permanente para esta coleção
Nível: início
Ano de início: 2014
Conceito atual na CAPES: 5
Ato normativo: Homologação da 85ª Reunião do CTC-ES, Parecer CNE/CES nº 163/2005.
Processo nº 23001.000081/2005-56 do Ministério da Educação.
Publicado no DOU 28/07/2005, seção 1, página 11)
Periodicidade de seleção: Anual
Área(s) de concentração: Química
Url do curso: https://quimica.vitoria.ufes.br/pt-br/pos-graduacao/PPGQ/detalhes-do-curso?id=956/a>
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Navegando Doutorado em Química por Autor "Almeida, Mariana Ramos de"
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- ItemAprendizagem de máquina na solução de problemas químicos: floresta aleatória aplicada à espectrometria na região do infravermelho(Universidade Federal do Espírito Santo, 2023-02-13) Nascimento, Márcia Helena Cassago; Filgueiras, Paulo Roberto; https://orcid.org/0000000326171601; http://lattes.cnpq.br/1907915547207861; https://orcid.org/0000-0001-5252-586X; http://lattes.cnpq.br/2620289110303573; Lima, Maria Tereza Weitzel Dias Carneiro; https://orcid.org/0000-0002-8731-5093; http://lattes.cnpq.br/9989703911201351; Ferrão, Marco Flôres; https://orcid.org/0000-0002-3332-0540; http://lattes.cnpq.br/7552747227876113; Almeida, Mariana Ramos de; https://orcid.org/0000-0002-2612-068X; http://lattes.cnpq.br/6690913086860156; Romão, Wanderson; https://orcid.org/0000000222546683; http://lattes.cnpq.br/9121022613112821; Oliveira, Marcone Augusto Leal deChemometrics began in the 1970s with the publication of a series of studies entitled "Computerized Learning Applied Machines to Chemical Problems" which express the motivation for the emergence of this field of study: the need for multivariate methods developed by chemists to solve chemical problems. Over 54 years, this area has expanded and presented solutions for increasingly complex data generated by modern Analytical Chemistry. Among the machine learning methods adapted to problems from the chemical point of view, this study contributes to a greater understanding, adaptation, and application of the random forest (RF) method. It is an ensemble-based method of learning multiple classifier systems. RF can be as a multivariate calibration model or pattern recognition, the latter being the focus of this thesis. In addition to the historical context, we describe adaptations proposed for the RF method to solve Chemistry problems with different analytical techniques and approaches. In this study, we applied RF for unsupervised pattern recognition as a screening method in a case study of suspected fuel fraud of diesel samples submitted to Fourier transform spectroscopy in the mid-infrared region (FT-MIR). The interpretation of the URF through a principal coordinate graph (PCoA) allowed the screening of samples with adulteration confirmed by the test of physical-chemical parameters. In addition, we adapted and applied the URF method to contribute to another field of study: biospectroscopy. A large part of the studies in this field is to develop alternative diagnosis methods or liquid biopsy. It is possible through biofluids, and spectroscopy associated with chemometric methods to extract information from biochemical changes caused by the disease or infectious agent. We adapted URF to identify a discriminant structure in spectroscopic data from two studies: a noninvasive diagnosis of COVID-19 from saliva samples analyzed by FT-MIR, and a proposal for pattern recognition and diagnosis of COVID-19 from nasopharyngeal swab and FT-MIR. In the first, an ensemble of classification models distinguished saliva samples from COVID19-infected people with an accuracy of 85%, a sensitivity of 93%, and a specificity of 74%. In another, URF was a comprehensive and innovative way: a starting point for selecting relevant variables and input data for classification models. With the URF as input data for classification models, we classified biofluid samples collected with two types of swabs with 87.6% accuracy, 93.6% sensitivity, 79.4% specificity, and 0.898 F-Score. Different approaches in this study contribute to disseminating the versatility and efficiency of the RF method, in addition to innovating its adaptation, taking advantage of the potential of this method for the different problems addressed.
- ItemExplorando métodos de seleção de variáveis e fusão de dados em regressão por vetores de suporte : uma aplicação em petroleômica(Universidade Federal do Espírito Santo, 2024-03-28) Cunha, Pedro Henrique Pereira da; Filgueiras, Paulo Roberto ; https://orcid.org/0000-0003-2617-1601; http://lattes.cnpq.br/1907915547207861; https://orcid.org/0000-0003-1850-4664; http://lattes.cnpq.br/7478317102047427; Souza, Murilo de Oliveira ; https://orcid.org/0000-0002-5299-564X; http://lattes.cnpq.br/1832643912229312; Duarte, Lucas Mattos; https://orcid.org/0000-0001-5133-6258; http://lattes.cnpq.br/0295291019523352; Almeida, Mariana Ramos de ; https://orcid.org/0000-0002-2612-068X; http://lattes.cnpq.br/6690913086860156; Romão, Wanderson ; https://orcid.org/0000-0002-2254-6683; http://lattes.cnpq.br/9121022613112821Support Vector Regression (SVR) is considered a black-box machine learning method and has stood out in chemometrics over the past decades, achieving results superior or equal to methods already established in academia. As a black-box method, it is challenging to understand the cause/effect relationship. To address this, variable selection can be applied, a strategy that aims to identify the most influential variables in building the model. This work proposes the development of two variable selection methods - Permutation Subwindow Analysis (SPA) and Noise-Incorporated Permutation Subwindow Analysis (NISPA) - to apply in SVR combined with infrared. SPA and NISPA provided the most accurate models for kinematic viscosity, saturates, and aromatic content. The root mean square error of prediction (RMSEP) for SPA and NISPA were, respectively, 14.3% and 14.6% for kinematic viscosity, 4.7% and 4.4% for saturates content, and 3.4% and 3.1% for aromatic content. Therefore, SPA and NISPA, in addition to generally obtaining faster, more accurate, and more parsimonious models, revealed the most important variables for building SVR models. Another way to improve a model is data fusion, but this strategy has been little studied in SVR. Thus, data fusion was studied using NIR, MIR, and NMR of ¹H and ¹³C combined using low, medium, and high-level fusion. The models generated by data fusion were superior to the models without fusion for most tests. In API density, the application of medium-level fusion using PCA combining MIR and NIR developed a model with better parameters than the model without data fusion. By applying medium level fusion with GA to predict pour point, combining NIR and NMR of ¹H, it was possible to surpass models without fusion, as well as models found in the literature. In total nitrogen, high-level fusion with MIR and NMR of ¹H proved to be statistically better than models without data fusion. This demonstrates that it is possible to extract new information for SVR modeling using data fusion and obtain statistically better models than those derived from isolated analytical sources
- ItemMétodos de aprendizagem de máquina em química analítica: Floresta Randômica aplicada na avaliação de petróleo(Universidade Federal do Espírito Santo, 2019-11-29) Lovatti, Betina Pires Oliveira; Filgueiras, Paulo Roberto; https://orcid.org/0000000326171601; http://lattes.cnpq.br/1907915547207861; https://orcid.org/0000-0002-7401-2739; http://lattes.cnpq.br/8227109432707028; Almeida, Mariana Ramos de; https://orcid.org/0000-0002-2612-068X; http://lattes.cnpq.br/6690913086860156; Romao, Wanderson; https://orcid.org/0000000222546683; http://lattes.cnpq.br/9121022613112821; Oliveira, Emanuele Catarina da Silva; https://orcid.org/; http://lattes.cnpq.br/1715851915787164; Cunha Neto, Alvaro ; https://orcid.org/0000-0002-1814-6214; http://lattes.cnpq.br/7448379486432052Technological development has driven chemical laboratories with instruments capable of extracting more information from samples. This has especially affected the area of Analytical Chemistry. The use of multivariate statistical methods, which is part of a growing area of Analytical Chemistry, called Chemometrics, helps to explore the full potential of these new instruments. At the forefront of Chemometrics is the new machine learning method: Random Forest (RF). This method has its applications aimed at modeling complex matrices such as petroleum. The complexity of oil is due to the wide variation in the composition of its constituents, which gives it distinct physicochemical properties. These compositional variations can be observed by spectroscopic techniques such as Mid Infrared (MIR) spectroscopy, Hydrogen Nuclear Magnetic Resonance (1H NMR) and Carbon (13C NMR) that have the potential to extract information at the molecular level of petroleum. Through the application of chemometric methods, this chemical information can be related to the physicochemical properties of petroleum. Thus, the present work aims to classify petroleum samples using spectroscopic techniques associated with machine learning methods, as well as, to explore the potentiality of the RF when combined with variable selection methods, and to identify in this algorithm variables that most contribute for the classification of oil samples. The results showed that RF was able to discriminate petroleum samples according to the Maximum Pour Point (PFM) from 1H and 13C NMR data. Besides, was possible to identify the variables that most contributed to the modeling, in which a balance between aromatic and saturated compounds was observed. In a second application, RF was efficient in discriminating 1H and 13C NMR spectra in relation to the total acidy number (TAN) of oil, especially when associated with the Principal Component Analysis (PCA) and Fisher's Discriminant (FD). The identification of the most important variables for discrimination showed a subtly greater contribution from the aromatic region. In the third application, the pattern recognition methods: PCA and k-Nearest Neighbors were efficient to identify oil profiles from MIR data. This process provides information on the chemical similarity of oils without the need for complete oil characterization